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Record W2921580168 · doi:10.5334/aogh.305

Employment Opportunities and Experiences among Recent Master’s-Level Global Health Graduates

2019· article· en· W2921580168 on OpenAlexaff
William Cherniak, Elahe Nezami, Quentin Eichbaum, Jessica Evert, Ashti Doobay-Persaud, Sharon Rudy, Ginny DeFrank, Tom Hall, Adam Hoverman

Bibliographic record

VenueAnnals of Global Health · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMarkham Stouffville HospitalUniversity of Toronto
Fundersnot available
KeywordsSalaryGraduation (instrument)WorkforcePublic healthGlobal healthCurriculumJob satisfactionMedical educationWork (physics)PsychologyPolitical scienceMedicineEconomic growthNursingEconomicsPedagogyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the job search, employment experiences, and job availability of recent global health-focused master's level graduates. METHODS: An online survey was conducted from October to December 2016 based out of Washington, DC. The study sample includes students graduating with master's degrees in global health, public health with a global health concentration or global medicine from eight U.S. universities. RESULTS: Out of 256 potential respondents, 152 (59%) completed the survey, with 102/152 (67%) employed. Of unemployed graduates, 38% were currently in another educational training program. Out of 91 employed respondents, 62 (68%) reported they had limitations or gaps in their academic training. The average salary of those employed was between $40,000 and $59,000 annually. The majority of respondents reported they currently work in North America (83.5%.); however, only 31% reported the desire to work in North America following graduation. CONCLUSIONS: Discrepancies exist between graduates' expectations of employment in global public health and the eventual job market. Communication between universities, students and employers may assist in curriculum development and job satisfaction for the global public health workforce.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.199
GPT teacher head0.421
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2019
Admission routes1
Has abstractyes

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